page_title: Cumulative accuracy profile
section_title: Example
hierarchical_section_title: Cumulative accuracy profile / Example
caption_attribution_description: English: The CAP profiles for the perfect, good and random model predicting the buying customers from a pool of 100 individuals.
context_page_description: The cumulative accuracy profile is used in data science to visualize the discriminative power of a model. The CAP of a model represents the cumulative number of positive outcomes along the y-axis versus the corresponding cumulative number of a classifying parameter along the x-axis. The CAP is distinct from the receiver operating characteristic, which plots the true-positive rate against the false-positive rate.
context_section_description: An example is a model that predicts whether a product is brought (positive outcome) by each individual from a group of people (classifying parameter) based on factors such as their gender, age, income etc. If group members would be contacted at random, the cumulative number of products sold would rise linearly toward a maximum value corresponding to the total number of buyers within the group. This distribution is called the "random" CAP. A perfect prediction, on the other hand, determines exactly which group members will buy the product, such that the maximum number of products sold will be reached with a minimum number of calls. This produces a steep line on the CAP curve that stays flat once the maximum is reached (contacting all other group members will not lead to more products sold), which is the "perfect" CAP.
  A successful model predicts the likelihood of individuals purchasing the product and ranks these probabilities to produce a list of potential customers to be contacted first. The resulting cumulative number of sold products will increase rapidly and eventually flatten out to the given maximum as more group members are contacted. This results in a distribution that lies between the random and the perfect CAP curves.
caption: The CAP profiles for the perfect, good and random model predicting the buying customers from a pool of 100 individuals.
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